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LFM2.5-Encoders for Fast Long-Context Inference on CPU
Hugging Face · Published · 3 min read
In 30 seconds
- What happened
- Liquid AI released two encoder models, LFM2.5-Encoder-230M and 350M, optimized for fast CPU inference on documents up to 8,192 tokens.
- Why it matters
- Engineers building classification, routing, or extraction systems that run continuously on existing hardware and need to process long documents cheaply.
- Watch out
- Benchmarks use PyTorch eager mode on unspecified CPU hardware. No ONNX or quantized exports yet, limiting production deployment options for high-volume serving.
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LFM2.5-Encoders for Fast Long-Context Inference on CPU
Team Article Published July 28, 2026
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Fernando Fernandes Neto fernandofernandes Follow
LiquidAI
Edoardo Mosca EdoardoMosca Follow
LiquidAI
Maxime Labonne mlabonne Follow
LiquidAI
Leonie Monigatti iamleonie Follow
LiquidAI
Today, we release two new encoder models on Hugging Face: LFM2.5-Encoder-230M and LFM2.5-Encoder-350M . They match the quality of larger models but stay fast as inputs get longer. This means you can run document-scale jobs on the hardware you already have, even on CPU.
Here's what you get:
Strong for their size: match or beat larger encoders on GLUE, SuperGLUE, and multilingual tasks.
8,192-token context with latency that grows slowly as inputs get longer.
Fast on CPU : about 3.7× faster than ModernBERT-base at long context.
With these, you can build intent routers, policy linters, PII detectors, and text classifiers that run cheaply, all day. See the live demos below.
Why we built a general-purpose encoder
Last month we released LFM2.5-Retrievers , built for multilingual search. LFM2.5-Encoders come from the same family but serve a broader purpose. They're pre-trained with a masked-language objective, so you can fine-tune them for classification, token-level tasks, and search alike. Search is just one thing an encoder enables. That's why we bu
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